code: def convert_like(tensor1, tensor2):
      """Convert a tensor to the same type as another.
  
      Args:
          tensor1 (tensor_like): tensor to convert
          tensor2 (tensor_like): tensor with corresponding type to convert to
  
      Returns:
          tensor_like: a tensor with the same shape, values, and dtype as ``tensor1`` and the
          same type as ``tensor2``.
  
      **Example**
  
      >>> x = np.array([1, 2])
      >>> y = tf.Variable([3, 4])
      >>> convert_like(x, y)
      <tf.Tensor: shape=(2,), dtype=int64, numpy=array([1, 2])>
      """
      interface = get_interface(tensor2)
  
      if interface == "torch":
          dev = tensor2.device
          return np.asarray(tensor1, device=dev, like=interface)
  
      return np.asarray(tensor1, like=interface)
  
file_path: pennylane/pennylane/math/utils.py
imports: import warnings
  import autoray
  import numpy
  from autograd.numpy.numpy_boxes import ArrayBox
  from autoray import numpy
  from .None import single_dispatch
label_desc: Use image node_id 5 for calling a global function with example usage: convert_like(tensor1, tensor2) and returns: np, np